Collect And Import Libraries¶

In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
In [3]:
anime_data = pd.read_csv('Top_Anime_data.csv')
In [4]:
anime_data.head()
Out[4]:
Score Popularity Rank Members Description Synonyms Japanese English Type Episodes ... Premiered Broadcast Producers Licensors Studios Source Genres Demographic Duration Rating
0 9.38 284 1 710 During their decade-long quest to defeat the D... Frieren at the Funeral 葬送のフリーレン Frieren: Beyond Journey's End TV 28 ... Fall 2023 Fridays at 23:00 (JST) Aniplex, Dentsu, Shogakukan-Shueisha Productio... None found, add some Madhouse Manga AdventureAdventure, DramaDrama, FantasyFantasy ShounenShounen 24 min. per ep. PG-13 - Teens 13 or older
1 9.09 3 2 3 After a horrific alchemy experiment goes wrong... Hagane no Renkinjutsushi: Fullmetal Alchemist,... 鋼の錬金術師 FULLMETAL ALCHEMIST Fullmetal Alchemist: Brotherhood TV 64 ... Spring 2009 Sundays at 17:00 (JST) Aniplex, Square Enix, Mainichi Broadcasting Sy... Funimation, Aniplex of America Bones Manga ActionAction, AdventureAdventure, DramaDrama, ... ShounenShounen 24 min. per ep. R - 17+ (violence & profanity)
2 9.07 13 3 2 Eccentric scientist Rintarou Okabe has a never... NaN STEINS;GATE Steins;Gate TV 24 ... Spring 2011 Wednesdays at 02:05 (JST) Frontier Works, Media Factory, Kadokawa Shoten... Funimation White Fox Visual novel DramaDrama, Sci-FiSci-Fi, SuspenseSuspense NaN 24 min. per ep. PG-13 - Teens 13 or older
3 9.06 342 4 630 Gintoki, Shinpachi, and Kagura return as the f... Gintama' (2015) 銀魂° Gintama Season 4 TV 51 ... Spring 2015 Wednesdays at 18:00 (JST) TV Tokyo, Aniplex, Dentsu Funimation, Crunchyroll Bandai Namco Pictures Manga ActionAction, ComedyComedy, Sci-FiSci-Fi ShounenShounen 24 min. per ep. PG-13 - Teens 13 or older
4 9.05 21 5 2 Seeking to restore humanity's diminishing hope... NaN 進撃の巨人 Season3 Part.2 Attack on Titan Season 3 Part 2 TV 10 ... Spring 2019 Mondays at 00:10 (JST) Production I.G, Dentsu, Mainichi Broadcasting ... Funimation Wit Studio Manga ActionAction, DramaDrama, SuspenseSuspense ShounenShounen 23 min. per ep. R - 17+ (violence & profanity)

5 rows × 22 columns

In [5]:
anime_data.columns
Out[5]:
Index(['Score', 'Popularity', 'Rank', 'Members', 'Description', 'Synonyms',
       'Japanese', 'English', 'Type', 'Episodes', 'Status', 'Aired',
       'Premiered', 'Broadcast', 'Producers', 'Licensors', 'Studios', 'Source',
       'Genres', 'Demographic', 'Duration', 'Rating'],
      dtype='object')
In [6]:
anime_data.shape
Out[6]:
(1000, 22)

Summary Statistic and Data Types¶

In [8]:
anime_data.describe().style.background_gradient(cmap = 'Blues')
Out[8]:
  Score Popularity Rank Members
count 1000.000000 1000.000000 1000.000000 1000.000000
mean 8.156580 1805.637000 500.500000 223.648000
std 0.284344 1888.308553 288.819436 246.288299
min 7.800000 1.000000 1.000000 1.000000
25% 7.920000 413.750000 250.750000 31.750000
50% 8.100000 1139.500000 500.500000 132.000000
75% 8.312500 2633.750000 750.250000 330.000000
max 9.380000 12043.000000 1000.000000 998.000000
In [9]:
anime_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 22 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   Score        1000 non-null   float64
 1   Popularity   1000 non-null   int64  
 2   Rank         1000 non-null   int64  
 3   Members      1000 non-null   int64  
 4   Description  1000 non-null   object 
 5   Synonyms     709 non-null    object 
 6   Japanese     999 non-null    object 
 7   English      859 non-null    object 
 8   Type         1000 non-null   object 
 9   Episodes     1000 non-null   object 
 10  Status       1000 non-null   object 
 11  Aired        1000 non-null   object 
 12  Premiered    569 non-null    object 
 13  Broadcast    569 non-null    object 
 14  Producers    1000 non-null   object 
 15  Licensors    1000 non-null   object 
 16  Studios      1000 non-null   object 
 17  Source       1000 non-null   object 
 18  Genres       771 non-null    object 
 19  Demographic  521 non-null    object 
 20  Duration     1000 non-null   object 
 21  Rating       1000 non-null   object 
dtypes: float64(1), int64(3), object(18)
memory usage: 172.0+ KB
In [10]:
anime_data.nunique()
Out[10]:
Score           121
Popularity     1000
Rank           1000
Members         481
Description     999
Synonyms        701
Japanese        953
English         856
Type              6
Episodes         84
Status            2
Aired           961
Premiered       129
Broadcast       184
Producers       641
Licensors        90
Studios         166
Source           15
Genres          237
Demographic       5
Duration        141
Rating            5
dtype: int64

Dealing With Missing Values¶

In [12]:
anime_data.isnull().sum()
Out[12]:
Score            0
Popularity       0
Rank             0
Members          0
Description      0
Synonyms       291
Japanese         1
English        141
Type             0
Episodes         0
Status           0
Aired            0
Premiered      431
Broadcast      431
Producers        0
Licensors        0
Studios          0
Source           0
Genres         229
Demographic    479
Duration         0
Rating           0
dtype: int64
In [13]:
#Create a copy of the df_anime
anime_data2 = anime_data.copy()
In [14]:
#replacing the NAN Values with English and Synonyms
anime_data2['Synonyms'].fillna(anime_data2['English'], inplace = True)
anime_data2['English'].fillna(anime_data2['Synonyms'],inplace = True)
In [15]:
#Get the unique value counts from Demographic Column
anime_data2['Demographic'].value_counts()
Out[15]:
Demographic
ShounenShounen    321
SeinenSeinen      126
ShoujoShoujo       53
JoseiJosei         16
KidsKids            5
Name: count, dtype: int64
In [16]:
#Get the unique value counts from Genres Column
anime_data2['Genres'].value_counts()
Out[16]:
Genres
ActionAction, AdventureAdventure, FantasyFantasy                                                            43
ComedyComedy, RomanceRomance                                                                                28
ActionAction, FantasyFantasy                                                                                23
ActionAction, Sci-FiSci-Fi                                                                                  22
DramaDrama, RomanceRomance                                                                                  21
                                                                                                            ..
AdventureAdventure, Award WinningAward Winning, ComedyComedy, DramaDrama, FantasyFantasy                     1
AdventureAdventure, FantasyFantasy, Slice of LifeSlice of Life                                               1
ActionAction, AdventureAdventure, Award WinningAward Winning, DramaDrama, FantasyFantasy, RomanceRomance     1
ActionAction, Award WinningAward Winning, Sci-FiSci-Fi                                                       1
ActionAction, AdventureAdventure, Award WinningAward Winning, DramaDrama, RomanceRomance, Sci-FiSci-Fi       1
Name: count, Length: 237, dtype: int64
In [17]:
#replacing the Genres and Demographic with the most frequent value
anime_data2['Demographic'].fillna('ActionAction, AdventureAdventure, FantasyFantasy', inplace = True)
anime_data2['Genres'].fillna('ShounenShounen', inplace = True)
In [18]:
#Drop few columns which are not necessary and have too much empty values
anime_data2.drop(['Broadcast','Premiered','Synonyms'],inplace = True,axis = 1)
In [19]:
#check the Dataframe
anime_data2.head()
Out[19]:
Score Popularity Rank Members Description Japanese English Type Episodes Status Aired Producers Licensors Studios Source Genres Demographic Duration Rating
0 9.38 284 1 710 During their decade-long quest to defeat the D... 葬送のフリーレン Frieren: Beyond Journey's End TV 28 Finished Airing Sep 29, 2023 to Mar 22, 2024 Aniplex, Dentsu, Shogakukan-Shueisha Productio... None found, add some Madhouse Manga AdventureAdventure, DramaDrama, FantasyFantasy ShounenShounen 24 min. per ep. PG-13 - Teens 13 or older
1 9.09 3 2 3 After a horrific alchemy experiment goes wrong... 鋼の錬金術師 FULLMETAL ALCHEMIST Fullmetal Alchemist: Brotherhood TV 64 Finished Airing Apr 5, 2009 to Jul 4, 2010 Aniplex, Square Enix, Mainichi Broadcasting Sy... Funimation, Aniplex of America Bones Manga ActionAction, AdventureAdventure, DramaDrama, ... ShounenShounen 24 min. per ep. R - 17+ (violence & profanity)
2 9.07 13 3 2 Eccentric scientist Rintarou Okabe has a never... STEINS;GATE Steins;Gate TV 24 Finished Airing Apr 6, 2011 to Sep 14, 2011 Frontier Works, Media Factory, Kadokawa Shoten... Funimation White Fox Visual novel DramaDrama, Sci-FiSci-Fi, SuspenseSuspense ActionAction, AdventureAdventure, FantasyFantasy 24 min. per ep. PG-13 - Teens 13 or older
3 9.06 342 4 630 Gintoki, Shinpachi, and Kagura return as the f... 銀魂° Gintama Season 4 TV 51 Finished Airing Apr 8, 2015 to Mar 30, 2016 TV Tokyo, Aniplex, Dentsu Funimation, Crunchyroll Bandai Namco Pictures Manga ActionAction, ComedyComedy, Sci-FiSci-Fi ShounenShounen 24 min. per ep. PG-13 - Teens 13 or older
4 9.05 21 5 2 Seeking to restore humanity's diminishing hope... 進撃の巨人 Season3 Part.2 Attack on Titan Season 3 Part 2 TV 10 Finished Airing Apr 29, 2019 to Jul 1, 2019 Production I.G, Dentsu, Mainichi Broadcasting ... Funimation Wit Studio Manga ActionAction, DramaDrama, SuspenseSuspense ShounenShounen 23 min. per ep. R - 17+ (violence & profanity)
In [20]:
#Final check for Null Values
anime_data2.isnull().sum()
Out[20]:
Score           0
Popularity      0
Rank            0
Members         0
Description     0
Japanese        1
English        47
Type            0
Episodes        0
Status          0
Aired           0
Producers       0
Licensors       0
Studios         0
Source          0
Genres          0
Demographic     0
Duration        0
Rating          0
dtype: int64
In [21]:
anime_data2 = anime_data2.dropna(subset=['English', 'Japanese'])
In [22]:
anime_data2.isnull().sum()
Out[22]:
Score          0
Popularity     0
Rank           0
Members        0
Description    0
Japanese       0
English        0
Type           0
Episodes       0
Status         0
Aired          0
Producers      0
Licensors      0
Studios        0
Source         0
Genres         0
Demographic    0
Duration       0
Rating         0
dtype: int64

Analyzing and Visualizing the data¶

In [24]:
#Finding out the Distribution & outliers of Score, popularity and Members
fig, axes = plt.subplots(3,2,figsize=(14,12))
#Score
sns.histplot(data = anime_data, x = 'Score',kde = True, ax = axes[0,0],color = 'skyblue')
axes[0,0].set_title('Score Distibution')
sns.boxplot(data = anime_data, x = 'Score', ax = axes[0,1],color = 'skyblue')
axes[0,1].set_title('Score Box Plot')

#Popularity
sns.histplot(data = anime_data, x = 'Popularity',kde = True, ax = axes[1,0],color = 'Salmon')
axes[1,0].set_title('Popularity Distibution')
sns.boxplot(data = anime_data, x = 'Popularity', ax = axes[1,1],color = 'Salmon')
axes[1,1].set_title('Popularity Box Plot')

#Members
sns.histplot(data = anime_data, x = 'Members',kde = True, ax = axes[2,0],color = 'lightgreen')
axes[2,0].set_title('Members Distibution')
sns.boxplot(data = anime_data, x = 'Popularity', ax = axes[2,1],color = 'lightgreen')
axes[2,1].set_title('Members Box Plot')

fig.suptitle('Distibution and Outliers of Score, Popularity & Members')
plt.tight_layout()
plt.show()
In [25]:
#correlation Matrix
Corr_data = anime_data2[['Score','Popularity','Rank','Members']].corr()
plt.figure(figsize=(10,8))
sns.heatmap(Corr_data, annot = True, cmap = 'magma',linewidths = 0.5,fmt=".2f")
plt.title('Correlation Matrix of Numerical Variables')
plt.show()
In [26]:
# scatter plots showing Popularity vs. Score colored by Demographic.
fig = px.scatter(anime_data2, x = 'Popularity',y = 'Score', color='Demographic', hover_name = 'English',title="Popularity vs. Score Colored by Demographic",
                labels={'Popularity': 'Popularity', 'Score': 'Score'},template = 'plotly_dark')
fig.update_layout(width=900, height=600)
fig.show()
In [27]:
#finding our Top 10 Genre
top_genres = anime_data2['Genres'].str.split(',').explode().str.strip().value_counts().head(10)
print("Top 10 Most Prevalent Genres:")
print(top_genres)

Top 10 Most Prevalent Genres:
Genres
ActionAction                346
DramaDrama                  278
FantasyFantasy              254
ComedyComedy                228
AdventureAdventure          221
ShounenShounen              214
Sci-FiSci-Fi                175
RomanceRomance              150
SupernaturalSupernatural    145
MysteryMystery              143
Name: count, dtype: int64
In [28]:
#finding out Top 10 Studios
top_studios = anime_data2['Studios'].str.split(',').explode().str.strip().value_counts().head(10)
print("Top 10 Studios Producing the Most Animes:")
print(top_studios)

Top 10 Studios Producing the Most Animes:
Studios
Madhouse             61
Production I.G       60
Sunrise              49
TMS Entertainment    48
Bones                40
A-1 Pictures         36
Kyoto Animation      36
Toei Animation       34
J.C.Staff            31
Shaft                30
Name: count, dtype: int64
In [29]:
#anime Popularity
anime_popularity = anime_data2[['English','Popularity']]
anime_popularity_sort = anime_popularity.sort_values(by = 'Popularity',ascending = True).head(20)
plt.figure(figsize=(16,8),dpi = 150)
sns.barplot(data = anime_popularity_sort, x = 'English', y = 'Popularity')
plt.title('Anime Popularity Top 20')
plt.xlabel('Title in English')
plt.xticks(rotation=90)
plt.show()
In [30]:
#average rating of anime with respect to episodes
average_rating = anime_data2.groupby('Episodes')['Score'].mean()
plt.figure(figsize=(12,8))
sns.lineplot(data = average_rating, x = average_rating.index, y = average_rating.values,marker = 'o')
plt.xlabel('Number of Episodes')
plt.ylabel('Average Rating')
plt.xticks(ticks=average_rating.index[::10], rotation=90)
plt.title('Average Rating by Number of Episodes')
plt.grid()
plt.show()
In [31]:
type_counts = anime_data2['Type'].value_counts()

type_counts_df = type_counts.reset_index()
type_counts_df.columns = ['Type', 'Count']

import plotly.express as px

fig = px.pie(type_counts_df, names='Type', values='Count', title='Distribution of Anime Types')
fig.update_traces(textinfo='percent+label', hole=0.3)
fig.update_layout(width=800, height=600)
fig.show()
In [ ]: